Virtual Restoration of Damaged Archeological Artifacts Obtained From Expeditions Using 3d Visualization

Shakya, Subarna

doi:10.36548/jiip.2019.2.005

Cited by 27 publications

(3 citation statements)

References 16 publications

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“…The use of 3D models to restore historical relics has become very important, so Shakya [ 1 ] proposed a method for virtual restoration of archaeological artifacts obtained from various missions. While this method provides an enhanced perception of damaged materials, more valuable historical artifacts can be repaired [ 1 ]. The patina has unique chemical and structural characteristics as a function of the specific characteristics of the environment.…”

Section: Related Workmentioning

confidence: 99%

“…It opens up a new research direction based on the analysis of the intrinsic structure of the 3D model. e innovations of this paper are as follows: (1) this paper improves the salient feature recognition method of 3D models of cultural relics. It built a standard template library of pottery cultural relics, analyzed the correlation between missing parts and standard parts from different dimensions such as shape, structure, texture, and semantics, and realizes automatic multifragment splicing of 3D models of damaged cultural relic fragments.…”

Section: Introductionmentioning

confidence: 99%

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3D Real Scene Data Collection of Cultural Relics and Historical Sites Based on Digital Image Processing

Zhao

2022

Computational Intelligence and Neuroscience

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Traditional digital geometry mainly focuses on local geometric features such as curvature and normal of 3D models. Although the curvature can describe the geometric curvature of the model surface, these local geometric properties cannot describe the global functional structure and associated properties of the 3D model. The purpose of this paper was to study the accurate splicing of cultural relic fragments based on the intrinsic structural features of the 3D model, such as geometry, texture, and function, whose method is the key to realizing the virtual reconstruction of damaged cultural relics. In this paper, a method for analyzing the intrinsic structure of 3D point cloud data is proposed, the feature representation method of 3D discrete curves and surfaces is studied, and a method for identifying geometric features of 3D point clouds based on the similarity measure of the principal curvature is proposed, which realizes the effective extraction of geometric features of the 3D point cloud model of cultural relics. By calculating the visual curvature distribution of the model under multiscale constraints, the effective extraction of the structural primitives of cultural relics with rich surface noise is realized. The experimental results in this paper show that the initial matching time is 1.416 seconds, the final matching time is 1.555 seconds, the average number of iterations is 13, the average stitching error is 1.7233 mm, and the standard deviation is 1.0265 mm. The experimental data show that the algorithm proposed in this chapter has good convergence characteristics and effectively avoids the divergence phenomenon that is easy to occur in the existing stitching algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Real Scene Data Collection of Cultural Relics and Historical Sites Based on Digital Image Processing

Zhao

2022

Computational Intelligence and Neuroscience

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“…SVM, Neural Network, Naïve Bayes and Decision Tree are some of classifiers used to select the right feature. In [11], the authors introduced a novel methodology which uses the heart beats of a human being as the input to determine fear. Here the ECG is required to find the emotions experienced by a person.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction using Fog Computing based Wireless Body Sensor Networks (WSNs)

Shakya¹,

Joby²

2021

JSWS

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Wireless Body Sensor Network (BSNs) are devices that can be worn by human beings. They have sensors with transmission, computation, storage and varying sensing qualities. When there are multiple devices to obtain data from, it is necessary to merge these data to avoid errors from being transmitted, resulting in a high quality fused data. In this proposed work, we have designed a data fusion approach with the help of data obtained from the BSNs, using Fog computing. Everyday activities are gathered in the form of data using an array of sensors which are then merged together to form high quality data. The data so obtained is then given as the input to ensemble classifier to predict heart-related diseases at an early stage. Using a fog computing environment, the data collector is established and the computation process is done with a decentralised system. A final output is produced on combining the result of the nodes using the fog computing database. A novel kernel random data collector is used for classification purpose to result in an improved quality. Experimental analysis indicates an accuracy of 96% where the depth is about 10 with an estimator count of 45 along with 7 features parameters considered.

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Structural Investigation on 3D Reality Based Models for Cultural Heritage Conservation and Virtual Restoration

Barsanti

2023

Digital Innovations in Architecture, Engineering and Construction

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Virtual Restoration of Damaged Archeological Artifacts Obtained From Expeditions Using 3d Visualization

Cited by 27 publications

References 16 publications

3D Real Scene Data Collection of Cultural Relics and Historical Sites Based on Digital Image Processing

3D Real Scene Data Collection of Cultural Relics and Historical Sites Based on Digital Image Processing

Heart Disease Prediction using Fog Computing based Wireless Body Sensor Networks (WSNs)

Structural Investigation on 3D Reality Based Models for Cultural Heritage Conservation and Virtual Restoration

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